Instructions to use diffusionbee/fooocus_inpainting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diffusionbee/fooocus_inpainting with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image # switch to "mps" for apple devices pipe = AutoPipelineForInpainting.from_pretrained("diffusionbee/fooocus_inpainting", dtype=torch.float16, device_map="cuda") img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" image = load_image(img_url).resize((1024, 1024)) mask_image = load_image(mask_url).resize((1024, 1024)) prompt = "a tiger sitting on a park bench" generator = torch.Generator(device="cuda").manual_seed(0) image = pipe( prompt=prompt, image=image, mask_image=mask_image, guidance_scale=8.0, num_inference_steps=20, # steps between 15 and 30 work well for us strength=0.99, # make sure to use `strength` below 1.0 generator=generator, ).images[0] - Notebooks
- Google Colab
- Kaggle
Download text_encoder_2/pytorch_model.bin from diffusionbee/fooocus_inpainting: direct link, hf CLI and curl.
- Browser
- Download file 1.39 GB
-
https://huggingface.co/diffusionbee/fooocus_inpainting/resolve/main/text_encoder_2/pytorch_model.bin
- Command line
-
hf download hf://diffusionbee/fooocus_inpainting/text_encoder_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/diffusionbee/fooocus_inpainting/resolve/main/text_encoder_2/pytorch_model.bin
1.39 GB
- Xet hash:
- 612d7248c5a3f1d3907653bb9dc10147e1c5a1734ac17ef9f1a559296a647222
- Size of remote file:
- 1.39 GB
- SHA256:
- 4bc8195d6dda6c00ec2a911e708ce82e5183e02811af751de596a44ac27b8ace
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